/*
 * Javlov - a Java toolkit for reinforcement learning with multi-agent support.
 * 
 * Copyright (c) 2009 Matthijs Snel
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package net.javlov;

import net.javlov.util.ArrayUtil;

/**
 * Agent implementing the Q-Learning algorithm. Exactly the same as the Sarsa algorithm
 * (and agent) except that the target in Q-Learning is
 * 
 * {@code r + max_a' Q(s',a')} instead of {@code r + Q(s',a')}.
 * 
 * @author Matthijs Snel
 *
 */
public class QLearningAgent extends SarsaAgent {

	/**
	 * Default constructor that does nothing.
	 */
	public QLearningAgent() {}
	
	/**
	 * Constructs agent with given value function and gamma = 0.9.
	 * @param v the value function to use.
	 */
	public QLearningAgent(QFunction q) {
		this(q, 0.9);
	}
	
	/**
	 * Constructs agent with given value function and gamma.
	 * @param v the value function to use.
	 * @param gamma the discountfactor gamma e [0, 1].
	 */
	public QLearningAgent(QFunction q, double gamma) {
		super(q, gamma);
	}
	
	/**
	 * Only difference between Sarsa and Q-Learning is the target value towards which
	 * Q-values get updated.
	 */
	@Override
	protected double getTargetValue(double[] qvalues, Action a) {
		return ArrayUtil.max(qvalues);
	}
}